Using 4D/RCS to Address AI Knowledge Integration

Abstract

In this article, we show how 4D/RCS incorporates and integrates multiple types of disparate knowledge representation techniques into a common, unifying architecture. The 4D/RCS architecture is based on the supposition that different knowledge representation techniques offer different advantages, and 4D/RCS is designed in such a way as to combine the strengths of all of these techniques into a common unifying architecture in order to exploit the advantages of each. In the context of applying the architecture to the control of autonomous vehicles, we describe the procedural and declarative types of knowledge that have been developed and applied and the value that each brings to achieving the ultimate goal of autonomous navigation. We also look at symbolic versus iconic knowledge representation and show how 4D/RCS accommodates both of these types of representations and uses the strengths of each to strive towards achieving human-level intelligence in autonomous systems.